Conciseness

The body is mostly command-focused and efficient, but the opening paragraph ('DeepL is a neural machine translation service that provides high-quality translations...') explains what DeepL is — a concept Claude already knows — and phrases like 'so you can focus on the integration logic rather than auth plumbing' add padding. This matches anchor 3 ('Mostly efficient but includes some unnecessary explanation or could be tightened'); it is above anchor 2 because the bulk is concrete commands rather than padded prose.

Actionability

The skill provides many concrete, executable bash commands (e.g. 'npm install -g @membranehq/cli@latest', 'membrane connection ensure "https://www.deepl.com/" --json') plus a Popular actions table and a run-command template, matching anchor 4 ('Mostly executable guidance; concrete code or commands with minor gaps'). It is not score 5 because the action-run examples rely on placeholders (CONNECTION_ID, <actionId>) and there is no complete worked example with sample input and output for a common case like translate-text.

Workflow Clarity

The connection flow is well sequenced with a state machine (READY/BUILDING/CLIENT_ACTION_REQUIRED/CONFIGURATION_ERROR), polling, and a poll-again feedback loop, but the running-actions workflow executes destructive operations such as 'delete-glossary' with no validation or verification checkpoint. Per the rubric, missing validation for destructive operations caps workflow_clarity at 3, so it cannot score 4 despite the strong connection steps.

Progressive Disclosure

The body is organized into clear, navigable sections (Overview, Authentication, Connecting to DeepL, Searching for actions, Popular actions, Running actions, Proxy requests, Best practices) with no nested or buried references and no bundle files, matching anchor 4 ('Good structure; most content is appropriately placed; references mostly clear; minor organization gaps'). It is not score 5 because there are no one-level-deep references to separate files (the anchor-5 example expects clearly signaled reference links), and not score 3 because the content is well structured rather than a monolithic wall or mis-organized.

Specificity

The description names the DeepL domain but its actions — 'Manage data, records, and automate workflows' — are generic boilerplate rather than concrete capabilities like translate, rephrase, or manage glossaries. This matches anchor 2 ('Names the domain but actions are minimal or generic') and is below anchor 3, which requires concrete actions such as 'extracts content'.

Completeness

Both 'what' ('DeepL integration. Manage data, records, and automate workflows.') and 'when' ('Use when the user wants to interact with DeepL data.') are explicitly present, matching anchor 4 ('Has both what and when; when could be more explicit or specific'). It is not score 3 because the 'when' is an explicit Use-when clause rather than weakly implied, and not score 5 because neither the what nor the when uses concrete trigger phrases like 'translate text' or 'manage glossaries'.

Trigger Term Quality

It includes the natural brand keyword 'DeepL' and a Use-when clause, but omits the most common natural terms users would actually say — 'translate', 'translation', 'rephrase' — matching anchor 3 ('Some relevant keywords but missing common variations or synonyms'). It is above anchor 2 because 'DeepL' is a specific, non-generic keyword rather than the generic 'Works with files'.

Distinctiveness Conflict Risk

Tying the skill to the specific DeepL product gives it a clear niche with minimal conflict risk, matching anchor 4 ('Mostly distinct; minor overlap risk with closely related skills'). It is not score 5 because the generic 'Manage data, records, and automate workflows' boilerplate overlaps with other Membrane integration skills, and not score 3 because the DeepL brand makes it more distinct than 'Works with document files'.